Two-dimensional (2D) to three-dimensional (3D) registration of a projection image to a volumetric data set typically involves three steps. First, a simulated projection image or digitally reconstructed radiograph (DRR) is computed given the current relative position of the projection source and the pre-operative volume. Second, a similarity measure and/or difference measure is computed for characterizing a metric to compare projection images to DRRs. Third, an optimization scheme searches through the parameter space, such as for six-dimensional rigid body motion, in order to maximize or minimize the similarity or difference measure. Once the optimum position is found, the DRR images are supposed to match the corresponding projection images.
2D to 3D registration is a well-researched topic. It is generally known to first to compute the DRRs from the volumetric image in a way that matches the real x-ray imaging results in terms of both brightness and contrast, and then to choose a well-behaved similarity or difference measure that can robustly characterize a comparison metric for the images (see L Zollei: “2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images”, Masters Thesis, MIT Al Lab, August 2001, hereinafter “Zollei”). Unfortunately, in order to make such an algorithm practical, the computational time has to be reduced.
State of the art implementations of such techniques suggest that for typical three-dimensional volume data sets, the computational time is on the order of a few minutes (see Zollei). Most of this time is spent on generating DRRs. The number of iterations over which these lengthy computations have to be done is also very important. In Zollei, the research suggests random sampling of the DRRs and performing computations based only on these random samples in order to decrease the computation load. The main goal is to reduce the computational complexity. However, this approach sacrifices robustness, since less information is available to the optimizer to take an accurate step toward the global solutions.
Aside from the intensity based registration method, there have been methods proposed in the literature that require a segmentation step. Feature-based registration methods have been heavily investigated for tissue images, and several such methods have been developed for vascular images. This class of methods includes surface-based methods (see A J Herline, J L Herring, J D Stefansic, W C Chapman, R L Galloway, B M Dawant, “Surface Registration for Use in Interactive, Image-Guided Liver Surgery”, Computer Aided Surgery: 5(1), 2000, pp. 11-17), iterative closest point algorithms (see P J Besi, N D McKay, “A method for registration of 3-D shapes”, IEEE Transactions on Pattern Analysis and Machine Intelligence: 14, 1992, pp. 239-56; see also Y Ge, C R Maurer Jr, J M Fitzpatrick, “Surface-based 3-D image registration using the Iterative Closest Point algorithm with a closest point transform”, Medical Imaging 1996: Image Processing, Proceedings of SPIE, 1996), and landmark-based techniques (see I Dryden, K Mardia, Statistical Shape Analysis, John Wiley and Sons, New York, N.Y., 1998). This class also includes 2D to 3D registration methods that attempt to determine how to project vessel structure from a 3D image so as to best match the vessel structure in a 2D projection image of the same anatomy (see E Bullitt, A Liu, S R Aylward, and S M Pizer, “Reconstruction of the intracerebral vasculature from MRA and a pair of projection views”, Information Processing in Medical Imaging, Pouitney, VT, 1997, pp. 537-542; see also E Bullitt, A Liu, S R Aylward, C Coffey, J Stone, S Mukherji, and S M Pizer “Registration of 3d cerebral vessels with 2d digital angiograms: Clinical evaluation”, Academic Radiology: 6, 1999, pp. 539-546). A model-based method is also described (see Stephen R. Ayiward, Julien Jornier, Sue Weeks, Elizabeth Bullitt, “Registration and Analysis of Vascular Images”, International Journal of Computer Vision, Volume 55, Issue 2-3 November-December 2003, Pages: 123-138, 2003).
For many practical applications, especially interventional scenarios, registration time is crucial. Performing registration in real-time or close to real-time is a highly desirable feature that enables such applications.